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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.18739 |
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| _version_ | 1866911857951899648 |
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| author | Abzaliev, Artem Espinosa, Humberto Pérez Mihalcea, Rada |
| author_facet | Abzaliev, Artem Espinosa, Humberto Pérez Mihalcea, Rada |
| contents | Similar to humans, animals make extensive use of verbal and non-verbal forms of communication, including a large range of audio signals. In this paper, we address dog vocalizations and explore the use of self-supervised speech representation models pre-trained on human speech to address dog bark classification tasks that find parallels in human-centered tasks in speech recognition. We specifically address four tasks: dog recognition, breed identification, gender classification, and context grounding. We show that using speech embedding representations significantly improves over simpler classification baselines. Further, we also find that models pre-trained on large human speech acoustics can provide additional performance boosts on several tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18739 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Dog Bark Decoding: Leveraging Human Speech Processing for Automated Bark Classification Abzaliev, Artem Espinosa, Humberto Pérez Mihalcea, Rada Computation and Language Similar to humans, animals make extensive use of verbal and non-verbal forms of communication, including a large range of audio signals. In this paper, we address dog vocalizations and explore the use of self-supervised speech representation models pre-trained on human speech to address dog bark classification tasks that find parallels in human-centered tasks in speech recognition. We specifically address four tasks: dog recognition, breed identification, gender classification, and context grounding. We show that using speech embedding representations significantly improves over simpler classification baselines. Further, we also find that models pre-trained on large human speech acoustics can provide additional performance boosts on several tasks. |
| title | Towards Dog Bark Decoding: Leveraging Human Speech Processing for Automated Bark Classification |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2404.18739 |